47,451 research outputs found
Multi-turn Inference Matching Network for Natural Language Inference
Natural Language Inference (NLI) is a fundamental and challenging task in
Natural Language Processing (NLP). Most existing methods only apply one-pass
inference process on a mixed matching feature, which is a concatenation of
different matching features between a premise and a hypothesis. In this paper,
we propose a new model called Multi-turn Inference Matching Network (MIMN) to
perform multi-turn inference on different matching features. In each turn, the
model focuses on one particular matching feature instead of the mixed matching
feature. To enhance the interaction between different matching features, a
memory component is employed to store the history inference information. The
inference of each turn is performed on the current matching feature and the
memory. We conduct experiments on three different NLI datasets. The
experimental results show that our model outperforms or achieves the
state-of-the-art performance on all the three datasets
Hypothesis Only Baselines in Natural Language Inference
We propose a hypothesis only baseline for diagnosing Natural Language
Inference (NLI). Especially when an NLI dataset assumes inference is occurring
based purely on the relationship between a context and a hypothesis, it follows
that assessing entailment relations while ignoring the provided context is a
degenerate solution. Yet, through experiments on ten distinct NLI datasets, we
find that this approach, which we refer to as a hypothesis-only model, is able
to significantly outperform a majority class baseline across a number of NLI
datasets. Our analysis suggests that statistical irregularities may allow a
model to perform NLI in some datasets beyond what should be achievable without
access to the context.Comment: Accepted at *SEM 2018 as long paper. 12 page
Visual Entailment Task for Visually-Grounded Language Learning
We introduce a new inference task - Visual Entailment (VE) - which differs
from traditional Textual Entailment (TE) tasks whereby a premise is defined by
an image, rather than a natural language sentence as in TE tasks. A novel
dataset SNLI-VE (publicly available at https://github.com/necla-ml/SNLI-VE) is
proposed for VE tasks based on the Stanford Natural Language Inference corpus
and Flickr30k. We introduce a differentiable architecture called the
Explainable Visual Entailment model (EVE) to tackle the VE problem. EVE and
several other state-of-the-art visual question answering (VQA) based models are
evaluated on the SNLI-VE dataset, facilitating grounded language understanding
and providing insights on how modern VQA based models perform.Comment: 4 pages, accepted by Visually Grounded Interaction and Language
(ViGIL) workshop in NeurIPS 201
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